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Vibration reliability analysis for aeroengine compressor blade based on support vector machine response surface method 认领 引用
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作者 高海峰 白广忱 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第5期1685-1694,共10页
To ameliorate reliability analysis efficiency for aeroengine components, such as compressor blade, support vector machine response surface method(SRSM) is proposed. SRSM integrates the advantages of support vector mac... To ameliorate reliability analysis efficiency for aeroengine components, such as compressor blade, support vector machine response surface method(SRSM) is proposed. SRSM integrates the advantages of support vector machine(SVM) and traditional response surface method(RSM), and utilizes experimental samples to construct a suitable response surface function(RSF) to replace the complicated and abstract finite element model. Moreover, the randomness of material parameters, structural dimension and operating condition are considered during extracting data so that the response surface function is more agreeable to the practical model. The results indicate that based on the same experimental data, SRSM has come closer than RSM reliability to approximating Monte Carlo method(MCM); while SRSM(17.296 s) needs far less running time than MCM(10958 s) and RSM(9840 s). Therefore,under the same simulation conditions, SRSM has the largest analysis efficiency, and can be considered a feasible and valid method to analyze structural reliability. 展开更多
关键词 vibration reliability analysis compressor blade support vector machine response surface method natural frequency
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Prediction method for surface finishing of spiral bevel gear tooth based on least square support vector machine 认领 引用 被引量：4
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作者 马宁 徐文骥 +2 位作者 王续跃 魏泽飞 庞桂兵 《Journal of Central South University》 SCIE EI CAS 2011年第3期685-689,共5页
The predictive model of surface roughness of the spiral bevel gear (SBG) tooth based on the least square support vector machine (LSSVM) was proposed.A nonlinear LSSVM model with radial basis function (RBF) kernel was ... The predictive model of surface roughness of the spiral bevel gear (SBG) tooth based on the least square support vector machine (LSSVM) was proposed.A nonlinear LSSVM model with radial basis function (RBF) kernel was presented and then the experimental setup of PECF system was established.The Taguchi method was introduced to assess the effect of finishing parameters on the gear tooth surface roughness,and the training data was also obtained through experiments.The comparison between the predicted values and the experimental values under the same conditions was carried out.The results show that the predicted values are found to be approximately consistent with the experimental values.The mean absolute percent error (MAPE) is 2.43% for the surface roughness and 2.61% for the applied voltage. 展开更多
关键词 pulse electrochemical finishing （PECF） surface roughness least squares support vector machine （LSSVM） prediction
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Multi-class classification method for strip steel surface defects based on support vector machine with adjustable hyper-sphere 认领 引用 被引量：2
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作者 Mao-xiang Chu Xiao-ping Liu +1 位作者 Rong-fen Gong Jie Zhao 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2018年第7期706-716,共11页
Focusing on strip steel surface defects classification, a novel support vector machine with adjustable hyper-sphere （AHSVM） is formulated. Meanwhile, a new multi-class classification method is proposed. Originated f... Focusing on strip steel surface defects classification, a novel support vector machine with adjustable hyper-sphere （AHSVM） is formulated. Meanwhile, a new multi-class classification method is proposed. Originated from support vector data description, AHSVM adopts hyper-sphere to solve classification problem. AHSVM can obey two principles： the margin maximization and inner-class dispersion minimization. Moreover, the hyper-sphere of AHSVM is adjustable, which makes the final classification hyper-sphere optimal for training dataset. On the other hand, AHSVM is combined with binary tree to solve multi-class classification for steel surface defects. A scheme of samples pruning in mapped feature space is provided, which can reduce the number of training samples under the premise of classification accuracy, resulting in the improvements of classification speed. Finally, some testing experiments are done for eight types of strip steel surface defects. Experimental results show that multi-class AHSVM classifier exhibits satisfactory results in classification accuracy and efficiency. 展开更多
关键词 Strip steel surface defect Multi-class classification Supporting vector machine Adjustable hyper-sphere
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Unbalanced classification method using least squares support vector machine with sparse strategy for steel surface defects with label noise 认领 引用 被引量：2
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作者 Li-ming Liu Mao-xiang Chu +1 位作者 Rong-fen Gong Xin-yu Qi 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2020年第12期1407-1419,共13页
Least squares support vector machine (LS-SVM) plays an important role in steel surface defects classification because of its high speed. However, the defect samples obtained from the real production line may be noise.... Least squares support vector machine (LS-SVM) plays an important role in steel surface defects classification because of its high speed. However, the defect samples obtained from the real production line may be noise. LS-SVM suffers from the poor classification performance in the classification stage when there are noise samples. Thus, in the classification stage, it is necessary to design an effective algorithm to process the defects dataset obtained from the real production line. To this end, an adaptive weight function was employed to reduce the adverse effect of noise samples. Moreover, although LSSVM offers fast speed, it still suffers from a high computational complexity if the number of training samples is large. The time for steel surface defects classification should be as short as possible. Therefore, a sparse strategy was adopted to prune the training samples. Finally, since the steel surface defects classification belongs to unbalanced data classification, LSSVM algorithm is not applicable. Hence, the unbalanced data information was introduced to improve the classification performance. Comprehensively considering above-mentioned factors, an improved LS-SVM classification model was proposed, termed as ILS-SVM. Experimental results show that the new algorithm has the advantages of high speed and great anti-noise ability. 展开更多
关键词 Steel surface defect Least squares support vector machine Anti-noise Sparseness Unbalanced data
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Support Vector Clustering Uncovered:Insights,Challenges,and Future Outlook 认领 引用
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作者 M.Tanveer Mohammad Tabish +2 位作者 Anuradha Kumari Ashwani Kumar Malik Weiping Ding 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第4期749-775,共27页
Support vector clustering(SVC)has emerged as a powerful unsupervised learning technique,derived from support vector machines(SVMs),offering a robust solution to a wide range of complex clustering challenges.Its unique... Support vector clustering(SVC)has emerged as a powerful unsupervised learning technique,derived from support vector machines(SVMs),offering a robust solution to a wide range of complex clustering challenges.Its unique ability to handle noise,outliers,and clusters of diverse,irregular shapes sets it apart from traditional clustering methods.SVC's distinct advantage lies in its capacity to autonomously determine the optimal number of clusters without prior topological knowledge of the data.SVC maps data to a higher-dimensional space,encloses it in a minimal sphere,and identifies clusters when mapped back,supporting complex shapes and ensuring optimality through kernel functions.This review paper provides a comprehensive analysis of the SVC algorithms,exploring their variants such as robust,sparse,and fuzzy-based models and adaptations for large-scale data.Moreover,we analyze the potential of twin support vector clustering(TWSVC),with an emphasis on the use of various loss functions.Finally,the paper explores emerging trends and outlines promising future research directions for both SVC and twin SVC.These include advancements in feature engineering,extension to semi-supervised and weakly supervised learning,and the integration of multi-view and multi-modal data.Our work aims to deepen the understanding of SVC,fostering advancements that address the evolving needs of clustering in real-world scenarios. 展开更多
关键词 Clustering kernel methods support vector machine(SVM) twin support vector clustering (TWSVC) unsupervised learning
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Effective forecast of Northeast Pacific sea surface temperature based on a complementary ensemble empirical mode decomposition–support vector machine method 认领 引用 被引量：2
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作者 LI Qi-Jie ZHAO Ying +1 位作者 LIAO Hong-Lin LI Jia-Kang 《Atmospheric and Oceanic Science Letters》 CSCD 2017年第3期261-267,共7页
The sea surface temperature （SST） has substantial impacts on the climate; however, due to its highly nonlinear nature, evidently non-periodic and strongly stochastic properties, it is rather difficult to predict SST... The sea surface temperature （SST） has substantial impacts on the climate; however, due to its highly nonlinear nature, evidently non-periodic and strongly stochastic properties, it is rather difficult to predict SST. Here, the authors combine the complementary ensemble empirical mode decomposition （CEEMD） and support vector machine （SVM） methods to predict SST. Extensive tests from several different aspects are presented to validate the effectiveness of the CEEMD-SVM method. The results suggest that the new method works well in forecasting Northeast Pacific SST at a 12-month lead time, with an average absolute error of approximately 0.3℃ and a correlation coefficient of 0.85. Moreover, no spring predictability barrier is observed in our experiments. 展开更多
关键词 Sea surface temperature complementary ensemble empirical mode decomposition support vector machine prediction
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Prediction of total nitrogen in water based on UV spectroscopy and Bayesian optimized least squares support vector machine 认领 引用 被引量：1
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作者 ZHENG Peichao YANG Qin +3 位作者 LI Chenglin YIN Xukun WANG Jinmei GUO Lianbo 《Optoelectronics Letters》 EI 2025年第11期698-704,共7页
The total nitrogen(TN)is a major factor contributing to eutrophication and is a crucial parameter in assessing surface water quality.Accurate and rapid methods are crucial for determining the TN content in water.Herei... The total nitrogen(TN)is a major factor contributing to eutrophication and is a crucial parameter in assessing surface water quality.Accurate and rapid methods are crucial for determining the TN content in water.Herein,a fast,highly sensitive,and pollution-free approach is proposed,which combines ultraviolet(UV)absorption spectroscopy with Bayesian optimized least squares support vector machine(LSSVM)for detecting TN content in water.Water samples collected from sampling points near the Yangtze River basin in Chongqing of China were analyzed using national standard methods to measure TN content as reference values.The prediction of TN content in water was achieved by integrating the UV absorption spectra of water samples with LSSVM.To make the model quickly and accurately select the optimal parameters to improve the accuracy of the prediction model,the Bayesian optimization(BO)algorithm was used to optimize the parameters of the LSSVM.Results show that the prediction model performs well in predicting TN concentration,with a high coefficient of prediction determination(R2=0.9413)and a low root mean square error of prediction(RMSE=0.0779 mg/L).Comparative analysis with previous studies indicates that the model used in this paper achieves lower prediction errors and superior predictive performance. 展开更多
关键词 Bayesian optimization eutrophication total nitrogen tn bayesian optimized least squares support vector machine lssvm least squares support vector machine assessing surface water water quality total nitrogen
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Time-variant reliability analysis of three-dimensional slopes based on Support Vector Machine method 认领 引用 被引量：5
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作者 陈昌富 肖治宇 张根宝 《Journal of Central South University》 SCIE EI CAS 2011年第6期2108-2114,共7页
In the reliability analysis of slope, the performance functions derived from the most available stability analysis procedures of slopes are usually implicit and cannot be solved by first-order second-moment approach. ... In the reliability analysis of slope, the performance functions derived from the most available stability analysis procedures of slopes are usually implicit and cannot be solved by first-order second-moment approach. A new reliability analysis approach was presented based on three-dimensional Morgenstem-Price method to investigate three-dimensional effect of landslide in stability analyses. To obtain the reliability index, Support Vector Machine （SVM） was applied to approximate the performance function. The time-consuming of this approach is only 0.028% of that using Monte-Carlo method at the same computation accuracy. Also, the influence of time effect of shearing strength parameters of slope soils on the long-term reliability of three-dimensional slopes was investigated by this new approach. It is found that the reliability index of the slope would decrease by 52.54% and the failure probability would increase from 0.000 705% to 1.966%. In the end, the impact of variation coefficients of c andfon reliability index of slopes was taken into discussion and the changing trend was observed. 展开更多
关键词 slope engineering Morgenstern-Price method three dimension Support Vector Machine time-variant reliability
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Casing life prediction using Borda and support vector machine methods 认领 引用 被引量：4
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作者 Xu Zhiqian Yan Xiangzhen Yang Xiujuan 《Petroleum Science》 SCIE CAS 2010年第3期416-421,共6页
Eight casing failure modes and 32 risk factors in oil and gas wells are given in this paper. According to the quantitative analysis of the influence degree and occurrence probability of risk factors, the Borda counts ... Eight casing failure modes and 32 risk factors in oil and gas wells are given in this paper. According to the quantitative analysis of the influence degree and occurrence probability of risk factors, the Borda counts for failure modes are obtained with the Borda method. The risk indexes of failure modes are derived from the Borda matrix. Based on the support vector machine （SVM）, a casing life prediction model is established. In the prediction model, eight risk indexes are defined as input vectors and casing life is defined as the output vector. The ideal model parameters are determined with the training set from 19 wells with casing failure. The casing life prediction software is developed with the SVM model as a predictor. The residual life of 60 wells with casing failure is predicted with the software, and then compared with the actual casing life. The comparison results show that the casing life prediction software with the SVM model has high accuracy. 展开更多
关键词 Support vector machine method Borda method life prediction model failure modes riskfactors
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Automated visual inspection of surface defects based on compound moment invariants and support vector machine 认领 引用 被引量：1
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作者 Zhang Xuewu Xu Lizhong +1 位作者 Ding Yanqiong Fan Xinnan 《High Technology Letters》 EI CAS 2012年第1期26-32,共7页
The traditional inspection methods are mostly based on manual inspection which is very likely to make erroneous judgments due to personal subjectivity or eye fatigue, and can＇t satisfy the accuracy. To overcome these... The traditional inspection methods are mostly based on manual inspection which is very likely to make erroneous judgments due to personal subjectivity or eye fatigue, and can＇t satisfy the accuracy. To overcome these difficulties, we develop a machine vision inspection system. We first compare several kinds of methods for feature extraction and classification, and then present a real-time automated visual inspection system for copper strips surface （CSS） defects based on compound moment invariants and support vector machine （SVM）. The proposed method first processes images collected by hardware system, and then extracts feature characteristics based on grayscale characteristics and morphologic characteristics （Hu and Zernike compound moment invariants）. Finally, we use SVM to classify the CSS defects. Furthermore, performance comparisons among SVM, back propagation （BP） and radial basis function （RBF） neural networks have been involved. Experimental results show that the proposed approach achieves an accuracy of 95.8% in detecting CSS defects. 展开更多
关键词 copper strips surface （CSS） defects compound invariant moments support vector machine（SVM） visual inspection system neural network
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Structural Reliability Analysis Based on Support Vector Machine and Dual Neural Network Direct Integration Method 认领 引用 被引量：1
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作者 NIE Xiaobo LI Haibin 《Journal of Donghua University(English Edition)》 CAS 2021年第1期51-56,共6页
Aiming at the reliability analysis of small sample data or implicit structural function,a novel structural reliability analysis model based on support vector machine(SVM)and neural network direct integration method(DN... Aiming at the reliability analysis of small sample data or implicit structural function,a novel structural reliability analysis model based on support vector machine(SVM)and neural network direct integration method(DNN)is proposed.Firstly,SVM with good small sample learning ability is used to train small sample data,fit structural performance functions and establish regular integration regions.Secondly,DNN is approximated the integral function to achieve multiple integration in the integration region.Finally,structural reliability was obtained by DNN.Numerical examples are investigated to demonstrate the effectiveness of the present method,which provides a feasible way for the structural reliability analysis. 展开更多
关键词 support vector machine(SVM) neural network direct integration method structural reliability small sample data performance function
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Krylov Iterative Methods for Support Vector Machines to Classify Galaxy Morphologies 认领 引用
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作者 Matthew Freed Jeonghwa Lee 《Journal of Data Analysis and Information Processing》 2015年第3期72-86,共15页
Large catalogues of classified galaxy images have been useful in many studies of the universe in astronomy. There are too many objects to classify manually in the Sloan Digital Sky Survey, one of the premier data sour... Large catalogues of classified galaxy images have been useful in many studies of the universe in astronomy. There are too many objects to classify manually in the Sloan Digital Sky Survey, one of the premier data sources in astronomy. Therefore, efficient machine learning and classification algorithms are required to automate the classifying process. We propose to apply the Support Vector Machine (SVM) algorithm to classify galaxy morphologies and Krylov iterative methods to improve runtime of the classification. The accuracy of the classification is measured on various categories of galaxies from the survey. A three-class algorithm is presented that makes use of multiple SVMs. This algorithm is used to assign the categories of spiral, elliptical, and irregular galaxies. A selection of Krylov iterative solvers are compared based on their efficiency and accuracy of the resulting classification. The experimental results demonstrate that runtime can be significantly improved by utilizing Krylov iterative methods without impacting classification accuracy. The generalized minimal residual method (GMRES) is shown to be the most efficient solver to classify galaxy morphologies. 展开更多
关键词 Data Mining Support Vector Machines Galaxy Morphologies Krylov Iterative Methods
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Application of Least Squares Support Vector Machine for Regression to Reliability Analysis 认领 引用 被引量：45
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作者 郭秩维 白广忱 《Chinese Journal of Aeronautics》 SCIE EI CAS 2009年第2期160-166,共7页
In order to deal with the issue of huge computational cost very well in direct numerical simulation, the traditional response surface method (RSM) as a classical regression algorithm is used to approximate a functiona... In order to deal with the issue of huge computational cost very well in direct numerical simulation, the traditional response surface method (RSM) as a classical regression algorithm is used to approximate a functional relationship between the state variable and basic variables in reliability design. The algorithm has treated successfully some problems of implicit performance function in reliability analysis. However, its theoretical basis of empirical risk minimization narrows its range of applications for... 展开更多
关键词 mechanism design of spacecraft support vector machine for regression least squares support vector machine for regression Monte Carlo method reliability implicit performance function
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Application of Support Vector Machine to Ship Steering 认领 引用 被引量：7
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作者 罗伟林 邹早建 李铁山 《Journal of Shanghai Jiaotong university(Science)》 EI 2009年第4期462-466,共5页
System identification is an effective way for modeling ship manoeuvring motion and ship manoeuvrability prediction. Support vector machine is proposed to identify the manoeuvring indices in four different response mod... System identification is an effective way for modeling ship manoeuvring motion and ship manoeuvrability prediction. Support vector machine is proposed to identify the manoeuvring indices in four different response models of ship steering motion, including the first order linear, the first order nonlinear, the second order linear and the second order nonlinear models. Predictions of manoeuvres including trained samples by using the identified parameters are compared with the results of free-running model tests. It is discussed that the different four categories are consistent with each other both analytically and numerically. The generalization of the identified model is verified by predicting different untrained manoeuvres. The simulations and comparisons demonstrate the validity of the proposed method. 展开更多
关键词 ship steering parameter identification response model support vector machine
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Probabilistic back analysis for geotechnical engineering based on Bayesian and support vector machine 认领 引用 被引量：3
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作者 陈炳瑞 赵洪波 +1 位作者 茹忠亮 李贤 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第12期4778-4786,共9页
Geomechanical parameters are complex and uncertain.In order to take this complexity and uncertainty into account,a probabilistic back-analysis method combining the Bayesian probability with the least squares support v... Geomechanical parameters are complex and uncertain.In order to take this complexity and uncertainty into account,a probabilistic back-analysis method combining the Bayesian probability with the least squares support vector machine(LS-SVM) technique was proposed.The Bayesian probability was used to deal with the uncertainties in the geomechanical parameters,and an LS-SVM was utilized to establish the relationship between the displacement and the geomechanical parameters.The proposed approach was applied to the geomechanical parameter identification in a slope stability case study which was related to the permanent ship lock within the Three Gorges project in China.The results indicate that the proposed method presents the uncertainties in the geomechanical parameters reasonably well,and also improves the understanding that the monitored information is important in real projects. 展开更多
关键词 geotechnical engineering back analysis uncertainty Bayesian theory least square method support vector machine(SVM)
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Gearbox Device Failure Mode Criticality Analysis Based on Support Vector Machine 认领 引用 被引量：2
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作者 李永华 李金颖 秦强 《Journal of Shanghai Jiaotong university(Science)》 EI 2016年第5期611-614,共4页
A method which integrates expert evaluation method and support vector machine(SVM) method is introduced for failure mode criticality analysis(FMCA) about the gearbox device. An expert evaluation standard is built by u... A method which integrates expert evaluation method and support vector machine(SVM) method is introduced for failure mode criticality analysis(FMCA) about the gearbox device. An expert evaluation standard is built by using expert evaluation method. The experts make scores about the gearbox failure mode. In order to overcome the subjectivity of expert evaluation method, we use SVM method to make a comprehensive prediction about the scores. According to the comprehensive prediction evaluation results, the FMCA of the gearbox device can be obtained. The analysis shows that the method used in this paper not only can effectively solve the problem which is unable to get specific failure rate in the qualitative analysis, but also can solve the problem which needs lots of data in the quantitative analysis. 展开更多
关键词 criticality expert evaluation method support vector machine(SVM) gearbox
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Improved Support Vector Machine Approach Based on Determining Thresholds Automatically 认领 引用
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作者 王晓华 闫雪梅 王晓光 《Journal of Beijing Institute of Technology》 EI CAS 2007年第3期300-304,共5页
To improve the training speed of support vector machine （SVM）, a method called improved center distance ratio method （ICDRM） with determining thresholds automatically is presented here without reduce the identific... To improve the training speed of support vector machine （SVM）, a method called improved center distance ratio method （ICDRM） with determining thresholds automatically is presented here without reduce the identification rate. In this method border vectors are chosen from the given samples by comparing sample vectors with center distance ratio in advance. The number of training samples is reduced greatly and the training speed is improved. This method is used to the identification for license plate characters. Experimental resuhs show that the improved SVM method-ICDRM does well at identification rate and training speed. 展开更多
关键词 support vector machine （SVM） improved center distance ratio method （ICDRM） threshold border vector
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An Approximate Linear Solver in Least Square Support Vector Machine Using Randomized Singular Value Decomposition 认领 引用
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作者 LIU Bing XIANG Hua 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2015年第4期283-290,共8页
In this paper, we investigate the linear solver in least square support vector machine（LSSVM） for large-scale data regression. The traditional methods using the direct solvers are costly. We know that the linear equ... In this paper, we investigate the linear solver in least square support vector machine（LSSVM） for large-scale data regression. The traditional methods using the direct solvers are costly. We know that the linear equations should be solved repeatedly for choosing appropriate parameters in LSSVM, so the key for speeding up LSSVM is to improve the method of solving the linear equations. We approximate large-scale kernel matrices and get the approximate solution of linear equations by using randomized singular value decomposition（randomized SVD）. Some data sets coming from University of California Irvine machine learning repository are used to perform the experiments. We find LSSVM based on randomized SVD is more accurate and less time-consuming in the case of large number of variables than the method based on Nystrom method or Lanczos process. 展开更多
关键词 least square support vector machine Nystr?m method Lanczos process randomized singular value decomposition
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Ensemble Nonlinear Support Vector Machine Approach for Predicting Chronic Kidney Diseases 认领 引用
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作者 S.Prakash P.Vishnu Raja +3 位作者 A.Baseera D.Mansoor Hussain V.R.Balaji K.Venkatachalam 《Computer Systems Science & Engineering》 SCIE EI 2022年第9期1273-1287,共15页
Urban living in large modern cities exerts considerable adverse effectson health and thus increases the risk of contracting several chronic kidney diseases (CKD). The prediction of CKDs has become a major task in urba... Urban living in large modern cities exerts considerable adverse effectson health and thus increases the risk of contracting several chronic kidney diseases (CKD). The prediction of CKDs has become a major task in urbanizedcountries. The primary objective of this work is to introduce and develop predictive analytics for predicting CKDs. However, prediction of huge samples isbecoming increasingly difficult. Meanwhile, MapReduce provides a feasible framework for programming predictive algorithms with map and reduce functions.The relatively simple programming interface helps solve problems in the scalability and efficiency of predictive learning algorithms. In the proposed work, theiterative weighted map reduce framework is introduced for the effective management of large dataset samples. A binary classification problem is formulated usingensemble nonlinear support vector machines and random forests. Thus, instead ofusing the normal linear combination of kernel activations, the proposed work creates nonlinear combinations of kernel activations in prototype examples. Furthermore, different descriptors are combined in an ensemble of deep support vectormachines, where the product rule is used to combine probability estimates ofdifferent classifiers. Performance is evaluated in terms of the prediction accuracyand interpretability of the model and the results. 展开更多
关键词 Chronic disease classification iterative weighted map reduce machine learning methods ensemble nonlinear support vector machines random forests
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Support vector machine with discriminative low-rank embedding 认领 引用
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作者 Guangfei Liang Zhihui Lai Heng Kong 《CAAI Transactions on Intelligence Technology》 SCIE EI 2024年第5期1249-1262,共14页
Support vector machine(SVM)is a binary classifier widely used in machine learning.However,neglecting the latent data structure in previous SVM can limit the performance of SVM and its extensions.To address this issue,... Support vector machine(SVM)is a binary classifier widely used in machine learning.However,neglecting the latent data structure in previous SVM can limit the performance of SVM and its extensions.To address this issue,the authors propose a novel SVM with discriminative low-rank embedding(LRSVM)that finds a discriminative latent low-rank subspace more suitable for SVM classification.The extension models of LRSVM are introduced by imposing different orthogonality constraints to prevent computational inaccuracies.A detailed derivation of the authors’iterative algorithms are given that is essentially for solving the SVM on the low-rank subspace.Additionally,some theorems and properties of the proposed models are presented by the authors.It is worth mentioning that the subproblems of the proposed algorithms are equivalent to the standard or the weighted linear discriminant analysis(LDA)problems.This indicates that the projection subspaces obtained by the authors’algorithms are more suitable for SVM classification compared to those from the LDA method.The convergence analysis for the authors proposed algorithms are also provided.Furthermore,the authors conduct experiments on various machine learning data sets to evaluate the algorithms.The experiment results show that the authors’algorithms perform significantly better than other algorithms,which indicates their superior abilities on classification tasks. 展开更多
关键词 iterative methods machine leaning support vector machunes
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